Among all relational operators the most difficult one to
process and optimize is the join. The
number of alternative plans to answer a query grows
exponentially with the number of joins
included in it. Further optimization effort is caused by the
support of a variety of join methods
(e.g., nested loop, hash join, merge join in Postgres) to process individual joins and a diversity of indices (e.g., r-tree, b-tree, hash in
Postgres) as access paths for
relations.

The current Postgres
optimizer implementation performs a near-exhaustive search over the space of
alternative strategies. This query optimization technique is
inadequate to support database application domains that involve
the need for extensive queries, such as artificial
intelligence.

The Institute of Automatic Control at the University of
Mining and Technology, in Freiberg, Germany, encountered the
described problems as its folks wanted to take the Postgres DBMS as the backend for a
decision support knowledge based system for the maintenance of
an electrical power grid. The DBMS needed to handle large
join queries for the inference machine
of the knowledge based system.

Performance difficulties in exploring the space of possible
query plans created the demand for a new optimization technique
being developed.

In the following we propose the implementation of a
Genetic Algorithm as an option for the
database query optimization problem.